Mars (Liyao) Gao

Ph.D. student
Paul G. Allen School of Computer Science & Engineering
University of Washington

Email: marsgao [at] uw [dot] edu
GitHub | Google Scholar

About me

I am a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Professor J. Nathan Kutz. My research focuses on AI for scientific discovery, developing interpretable and generalizable learning frameworks for complex spatiotemporal systems. I work at the intersection of deep learning, physics learning, and scientific computing, aiming to uncover governing equations and enable reliable long-term prediction to accelerate scientific discovery. My long-term goal is to build robust machine learning methods that can bridge data and fundamental governing laws.

News

[Mar. 2025] Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks is now available at the Proceedings of the National Academy of Sciences (PNAS) [paper] New

[Apr. 2025] UQ-SHRED paper is now available on arXiv, joint work with Yuxuan Bao, Amy S. Rude, Xinwei Shen, and J. Nathan Kutz. [arXiv] New

[Apr. 2025] Invited talk @ UCSB Applied Math seminar, UW CS4Env, and MIT in Marin Soljačić's group.

[Oct. 2024] Invited talk @ Georgia Tech ACMS seminar.

[Mar. 2024] Our paper "Bayesian autoencoders for data-driven discovery of coordinates, governing equations, and fundamental constants," is now published in PRSA!

Selected publications

Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks.
Mars L. Gao, Jan P. Williams, J. Nathan Kutz.
GitHub Colab Website Youtube
Proceedings of the National Academy of Sciences (PNAS).

Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants.
Mars L. Gao, J. Nathan Kutz.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Convergence of uncertainty estimates in ensemble and Bayesian sparse model discovery.
Mars L. Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz.
Under review at Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

Deformation Robust Roto-Scale-Translation Equivariant CNNs.
Mars L. Gao, Wei Zhu, Guang Lin.
Transaction of Machine Learning Research.

On Optimal Early Stopping: Over-informative versus Under-informative Parametrization.
Ruoqi Shen, Mars L. Gao, Yi-An Ma.
arxiv.